BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

148 related articles for article (PubMed ID: 38787844)

  • 1. Automatic exudate and aneurysm segmentation in OCT images using UNET++ and hyperreflective-foci feature based bagged tree ensemble.
    Tanthanathewin R; Wongrattanapipat W; Khaing TT; Aimmanee P
    PLoS One; 2024; 19(5):e0304146. PubMed ID: 38787844
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Hyper-reflective foci segmentation in SD-OCT retinal images with diabetic retinopathy using deep convolutional neural networks.
    Yu C; Xie S; Niu S; Ji Z; Fan W; Yuan S; Liu Q; Chen Q
    Med Phys; 2019 Oct; 46(10):4502-4519. PubMed ID: 31315159
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Multimodality analysis of Hyper-reflective Foci and Hard Exudates in Patients with Diabetic Retinopathy.
    Niu S; Yu C; Chen Q; Yuan S; Lin J; Fan W; Liu Q
    Sci Rep; 2017 May; 7(1):1568. PubMed ID: 28484225
    [TBL] [Abstract][Full Text] [Related]  

  • 4. OCT Hyperreflective Retinal Foci in Diabetic Retinopathy: A Semi-Automatic Detection Comparative Study.
    Midena E; Torresin T; Velotta E; Pilotto E; Parrozzani R; Frizziero L
    Front Immunol; 2021; 12():613051. PubMed ID: 33968016
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Fast and Automated Hyperreflective Foci Segmentation Based on Image Enhancement and Improved 3D U-Net in SD-OCT Volumes with Diabetic Retinopathy.
    Xie S; Okuwobi IP; Li M; Zhang Y; Yuan S; Chen Q
    Transl Vis Sci Technol; 2020 Apr; 9(2):21. PubMed ID: 32818082
    [TBL] [Abstract][Full Text] [Related]  

  • 6. A location-to-segmentation strategy for automatic exudate segmentation in colour retinal fundus images.
    Liu Q; Zou B; Chen J; Ke W; Yue K; Chen Z; Zhao G
    Comput Med Imaging Graph; 2017 Jan; 55():78-86. PubMed ID: 27665058
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Automatic Segmentation of Hyperreflective Foci in OCT Images Based on Lightweight DBR Network.
    Wei J; Yu S; Du Y; Liu K; Xu Y; Xu X
    J Digit Imaging; 2023 Jun; 36(3):1148-1157. PubMed ID: 36749455
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Hard exudates segmentation based on learned initial seeds and iterative graph cut.
    Kusakunniran W; Wu Q; Ritthipravat P; Zhang J
    Comput Methods Programs Biomed; 2018 May; 158():173-183. PubMed ID: 29544783
    [TBL] [Abstract][Full Text] [Related]  

  • 9. A computer-aided diagnostic system for detecting diabetic retinopathy in optical coherence tomography images.
    ElTanboly A; Ismail M; Shalaby A; Switala A; El-Baz A; Schaal S; Gimel'farb G; El-Azab M
    Med Phys; 2017 Mar; 44(3):914-923. PubMed ID: 28035657
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Double-branched and area-constraint fully convolutional networks for automated serous retinal detachment segmentation in SD-OCT images.
    Gao K; Niu S; Ji Z; Wu M; Chen Q; Xu R; Yuan S; Fan W; Chen Y; Dong J
    Comput Methods Programs Biomed; 2019 Jul; 176():69-80. PubMed ID: 31200913
    [TBL] [Abstract][Full Text] [Related]  

  • 11. An ensemble classification of exudates in color fundus images using an evolutionary algorithm based optimal features selection.
    Ullah H; Saba T; Islam N; Abbas N; Rehman A; Mehmood Z; Anjum A
    Microsc Res Tech; 2019 Apr; 82(4):361-372. PubMed ID: 30677193
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Epiretinal Membrane Detection at the Ophthalmologist Level using Deep Learning of Optical Coherence Tomography.
    Lo YC; Lin KH; Bair H; Sheu WH; Chang CS; Shen YC; Hung CL
    Sci Rep; 2020 May; 10(1):8424. PubMed ID: 32439844
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Detection of hard exudates in retinal images using a radial basis function classifier.
    García M; Sánchez CI; Poza J; López MI; Hornero R
    Ann Biomed Eng; 2009 Jul; 37(7):1448-63. PubMed ID: 19430906
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Decorrelation Signal of Diabetic Hyperreflective Foci on Optical Coherence Tomography Angiography.
    Murakami T; Suzuma K; Dodo Y; Yoshitake T; Yasukura S; Nakanishi H; Fujimoto M; Oishi M; Tsujikawa A
    Sci Rep; 2018 Jun; 8(1):8798. PubMed ID: 29892079
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Automatic segmentation of hyperreflective foci in OCT images.
    Varga L; Kovács A; Grósz T; Thury G; Hadarits F; Dégi R; Dombi J
    Comput Methods Programs Biomed; 2019 Sep; 178():91-103. PubMed ID: 31416566
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Automatic segmentation of abnormal capillary nonperfusion regions in optical coherence tomography angiography images using marker-controlled watershed algorithm.
    Ganjee R; Moghaddam ME; Nourinia R
    J Biomed Opt; 2018 Sep; 23(9):1-16. PubMed ID: 30264553
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Classification of diabetes-related retinal diseases using a deep learning approach in optical coherence tomography.
    Perdomo O; Rios H; Rodríguez FJ; Otálora S; Meriaudeau F; Müller H; González FA
    Comput Methods Programs Biomed; 2019 Sep; 178():181-189. PubMed ID: 31416547
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Fully automated detection of retinal disorders by image-based deep learning.
    Li F; Chen H; Liu Z; Zhang X; Wu Z
    Graefes Arch Clin Exp Ophthalmol; 2019 Mar; 257(3):495-505. PubMed ID: 30610422
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Automated Segmentation of Retinal Fluid Volumes From Structural and Angiographic Optical Coherence Tomography Using Deep Learning.
    Guo Y; Hormel TT; Xiong H; Wang J; Hwang TS; Jia Y
    Transl Vis Sci Technol; 2020 Oct; 9(2):54. PubMed ID: 33110708
    [TBL] [Abstract][Full Text] [Related]  

  • 20. EAD-Net: A Novel Lesion Segmentation Method in Diabetic Retinopathy Using Neural Networks.
    Wan C; Chen Y; Li H; Zheng B; Chen N; Yang W; Wang C; Li Y
    Dis Markers; 2021; 2021():6482665. PubMed ID: 34512815
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 8.